Scripts for Power analysis of Bivariate ACE in Twin Sample?
joz
Joined: 06/30/2015
Hello,
This is my first time using this forum...any help is greatly appreciated!
Are there any OpenMx scripts for determining power in a twin sample using bivariate ACE analyses?
In a twin sample, I'm conducting bivariate ACE analysis of a personality trait and depressive symptoms. I found 4.6% in shared additive genetic variance, which is smaller than expected (compared to previous reports in the literature)
My univariate ACE results: A^2 for personality trait = 62%; A^2 for depressive symptoms = 32%
My sample size is on the small side (MZ=112, DZ=100; total N=212), so I'm trying to determine if the relatively small effect size is due to power issues. I'd like to assess power of my sample.
Are there any OpenMx R scripts for assessing power in a twin sample using bivariate ACE ?
OpenMx version: 2.0.1.4157
R version: R version 3.1.3 (2015-03-09)
Platform: x86_64-apple-darwin10.8.0
Default optimiser: NPSOL
Power or Confidence Interval?
/* I had been emailing with Joyce off the forums prior to this post, and this was my email response. */
Hi Joyce,
I'm not sure what you mean by "power" in this context, but if you want a sense of the range of possible values of the variance components then you can get confidence intervals on them.
If you want a confidence interval on all the elements in a matrix/algebra called "A", then do something like this
ciA <- mxCI("A")
yourModel <- mxModel(other stuff, ..., ciA)
yourFit <- mxRun(yourModel, intervals=TRUE)
summary(yourFit)
In the multigroup case, you can put the ciA object in the model where "A" lives.
Log in or register to post comments